In recent years, industry trends toward mergers and acquisitions has forced most OEMs to re-think their processes to account for the multitude of data sources used by the various corporate divisions within the enterprise for storing and manipulating product data, including the product bill of materials, parts catalog, diagnostic procedures and warranty claims as a partial list of the kinds of product data that exists.
Government regulations, rising production cost, shorter time-to-market are yet other reasons why companies are looking for a more adaptive and dynamic information technology (IT) infrastructure that would scale to the on-demand era for product life cycle management.
The complexity of the IT environment in the industrial manufacturing sector has grown exponentially over the years creating problems. Some attributes of these problems include:
The increasingly complex nature of the product itself as measured by the number of components that goes into the making of a product and the umber of configurations for each product. This translates into increased storage and processing capacities for managing the information associated with the product design that could be in the order of a terabyte for just one product model.
The lack of visibility across the product life cycle due to the disparity of data management systems used in the different stages of the design, development, manufacturing process and services after sales. A large number of heterogeneous systems that are in use throughout the extended enterprise create an artificial barrier for information sharing.
The product design is often organized in silos around specific product assemblies (in the case of automobiles, e.g., wing design, engine design, body structure design, interior design, etc) making it difficult to integrate data across multiple divisions. While the project team is often composed of a multi-disciplinary group of engineers, the IT tools remain too fragmented and specifically tailored to the organization or division that uses them the most.
In this environment, traditional approaches for data integration using data warehousing does not always scale well. A data warehouse is a copy of transaction data specifically structured for querying, analyzing, and reporting. A data warehouse can be normalized or denormalized. It can be a relational database, flat file, hierarchical database, object database, etc. Data warehouse data often gets changed. Data warehouses often focus on a specific activity or entity. Data warehouses are usually designed to meet the requirements of one specific application and are not easily extensible without tearing down and rebuilding the table schema. They provide a fixed view on the data and are not easily adapted to changing business needs such as when new suppliers are integrated into the value chain.
Furthermore, product data tends to be deeply hierarchical in nature and has associated semantics and access control procedures that are encapsulated within the data management system that hosts the information and cannot be easily exposed to external applications. In order to safeguard the integrity of the data that is owned by any given partner, the industry had traditionally resorted to product data exchange where each partner exports a subset of the data that is stored within its domain and shares the data with other partners by mean of data replication. This approach tends to be slow and costly as multiple iterations may be required to provide the information that is needed. The approach leads to data redundancy where multiple replicas of the same work product could exist within the extended enterprise and requires additional complexity for managing the life-cycle of the exchanged information.
As industry transforms its processes to better leverage the resources and know-how of the extended enterprise, a new approach based on data federation emerges as it promises to deliver on speed and accuracy, both of which are needed to quickly predict and pinpoint weaknesses in a product design and performance. Delivering on such a promise requires a better understanding of the semantic and data models that are prominently used in the industry.
In the following description the automotive industry will be used as an illustrative example of an application of the present invention. However, the invention is not to be construed as being limited solely to the automotive industry. A generic product structure is a hierarchical structure of generic concepts or functions such as the vehicle body structure or the vehicle hydraulic system. The generic product structure describes a logical aggregation of the vehicle assemblies and serves as a template for creating the detailed product structure. As such, the generic product structure can be used to define the common concepts (e.g., seats) that are shared among similar product classes (e.g., SUV and passenger cars).
An ontology is a specification of a conceptionalization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. A common ontology defines a vocabulary with which queries and assertions are exchanged among agents. A commitment to a common ontology is a guarantee of consistency, but not completeness, with respect to queries and assertions using the vocabulary defined in the ontology. An automotive vehicle ontology is an annotated meta model of the generic product structure, and processes that can execute against such structure. It augments the generic product structure with various relationships and dependencies that may exist between the different components but cannot otherwise be expressed in the generic product structure or the detailed product structure. For example, the generic product structure for a vehicle may contain a placeholder for the wheel assembly. The vehicle ontology augments this assertion by defining a “similar to” relationship between the wheel assembly of a sports same product class. One derived benefit of such ontological relation is to broaden the scope of the search to a wider set of data sources that otherwise would not have been considered.
The vehicle ontology provides the foundation for defining a common semantic model of the product structure with all the associated engineering processes that execute against the product structure as shareable business objects. FIG. 15 is a block diagram of one possible vehicle ontology.
Reducing warranty costs by conducting a deep failure analysis and improved claim processes have been identified as a strategic initiative by many in the automotive industry. While OEMs continues to strive to manage warranty payout while improving supplier recovery for failed parts, they recognize the value of proactive failure. identification. The objectives of these efforts are to reduce the cost of warranty through identification of warranty issues more quickly than has been previously achieved and thereby reducing costs; and enhancement of brand loyalty by demonstrating a commitment to the reliability and quality of products carrying the brand name.
Earl warning and failure analysis solutions focus on applying data mining and analytics against a wider set of data sources including warranty claims, call-center contacts, vehicle bills of materials, supplier parts catalog, suppliers' bulletins and other attributes for how and where the vehicle is used. The analytics aims at identifying trends, patterns, and abnormalities at an early stage and creating a knowledge model that can be used by the quality engineers to anticipate any major recall.
The present invention provides a system and method for planning and generating queries for multi-dimensional analysis across divisions in an entity using domain models and data integration.